Triple
T4292723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosario |
E99633
|
entity |
| Predicate | rankInArgentinaByPopulation |
P1026
|
FINISHED |
| Object | third-largest city |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: third-largest city | Statement: [Rosario, rankInArgentinaByPopulation, third-largest city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInArgentinaByPopulation Context triple: [Rosario, rankInArgentinaByPopulation, third-largest city]
-
A.
hasPopulationRankInChile
Indicates the relative position of an entity in the ordered ranking of populations within Chile.
-
B.
unitArgentina
Indicates a relationship where an entity is associated with, belongs to, or is characterized as a unit related to Argentina.
-
C.
populationRankInMexico
Indicates the relative position of an entity in terms of population size compared to other entities within Mexico.
-
D.
hasPopulationRank
chosen
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
E.
largestArgentineBase
Indicates that the subject is the largest Argentine base (e.g., by size, capacity, or another specified measure) among a relevant set of bases.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69b3455175088190aa79c6e03b86647e |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35082228081908504e3fd7c4ca1e8 |
completed | March 12, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69b347fe55a88190b77bab0c0f38e1aa |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:08 p.m.